epidemics michael ford simon krueger 1. it’s just like telephone! 2

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Epidemics

Michael FordSimon Krueger

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IT’S JUST LIKE TELEPHONE!

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Epidemic Convergence

• If there are n nodes and each node gossips to log(n)+k other nodes on average, then the probability that everyone gets the message converges to e^(-e^(-k)).

• A. Ganesh, A.-M. Kermarrec and L. Massoulie, Peer-to-peer membership management for gossip-based protocols, IEEE Transactions on Computers 52 (2003) (2), pp. 139–149.

• P. Erdös and A. Renyi, “On the Evolution of Random Graphs,” Mat Kutato Int. Közl, vol. 5, no. 17, pp. 17-60, 1960.

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Bimodal Multicast (pbcast)

Kenneth P. Birman, Mark Hayden, Oznur Ozkasap, Zhen Xiao, Mihai

Budiu, and Yaron Minsky

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Motivation

• Best-Effort Protocols– Increased scalability– No end-to-end delivery guarantee– Hard to reason about system state during failures

• Reliable Protocols– Strong atomic guarantees – “all or nothing”– Throughput is not resilient to slow nodes• One bad apple spoils the bunch

– Background overhead reaches “meltdown” levels

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Throughput Stability

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Bimodal Multicast (pbcast)

• Atomicity – almost all or almost none • Throughput Stability – low variance• Ordering – per sender FIFO• Multicast Stability – minimal message buffer• Lost Message Detection• Scalability – “Costs are constant or grow

slowly as a function of the network size”

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Pbcast details

• Best-effort broadcast– IP Multicast– or Multicast Tree

• Anti-entropy– Gossip a message list summary– Detect message loss– Pull messages if needed• Why not push?

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Pbcast Example

Note: Broadcast and Anti-entropy stages occur concurrently.

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Assumptions

• Faults– Network errors are independent and identically

distributed– Known, bounded, link delays– No Byzantine failures

• System– Each node knows every other node

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Computational Results

• Bcast unsuccessful • 5% message loss• 0.1% crash rate for run

• What is the ideal shape?

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Rounds to Delivery

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Issues

• Are slow processes always going to fall behind and slow down other processes?

• What if a processes receives multiple message queries?

• How do you determine when to stop buffering a message? (Scalability)

• Random gossip through a router can be a bottleneck.

• How does membership management affect scalability?

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Optimizations1) Soft-Failure Detection – Retransmit only in the round

that request was received2) Round retransmission limit – Cap data per round3) Cyclic Retransmissions – Avoid repeat message

retransmissions from previous rounds4) Most-Recent-First Retransmission – Stops processes

from permanently lagging5) Independent Numbering of Rounds – No

synchronization needed among processes6) Random Graphs for Scalability – Gossip only to a

subset of the processes7) Multicast for Some Retransmissions – Multicast upon

receiving multiple requests for the same message

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Comparison to a Strong Protocol

The effects of Soft faults on Throughput

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Effects of Network Congestion

The effects of Link faults on Throughput

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Comparison to SRM

Why compare pbcast to SRM (a reliable protocol) and not a best effort protocol?

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QUESTIONS?

Exploring the Energy-Latency Trade-off for Broadcasts in Energy-Saving Sensor Networks

M. Miller, C. Sengul, I. Gupta, ICDCS 2005Presented By

Simon Krueger

Outline1. Motivation and Background

– The Problem

– Existing Solutions

2. Core Ideas

– Probability-Based Broadcast Forwarding

3. Experimental Results

– Reliability

– Energy

– Latency

– Energy-Latency Trade-off

4. Discussion

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The Problem

• Wireless Sensor Networks (WSNs) use Motes that have a battery lifetime of a few weeks

• Message broadcast is useful for applications in WSNs

– Disseminating software updates (e.g., Trickle)

– Forwarding sensor observations

• Increasing reliability and performance causes greater depletion of battery

• Designers need flexibility between reliability and performance

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Existing Solution(s): Energy Efficient Medium Access Control (MAC) protocols• Active-sleep cycle

– Active Time

– Sleep Time

1. IEEE 802.11 Power Safe Mode (PSM)

– Synchronized active sleep schedule

2. S-MAC

– Virtual clusters of synchronized active sleep schedules

3. T-MAC

– Dynamic active sleep schedule

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Broadcast in IEEE 802.11 PSM

Node 1

Node 2

Node 3

B

A D1 B

A

A D1

D1

B

ATIM window ATIM window ATIM window

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1

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Probability-Based Broadcast Forwarding (PBBF)

• Design a broadcast protocol on top of existing energy efficient MAC layer protocols that allows a designer to tune energy and latency at different levels of reliability

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PBBF Adds Two New Parameters

1.p is the probability that a node rebroadcasts a packet immediately

2.q is the probability that a given node stays awake instead of sleeping

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PBBF Demonstration

Node 1

Node 2

Node 3

B

B

A D

ID B

ATIM window

ID

ATIM window ATIM window

p

q

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1

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p

PBBF (cont.)• p presents a tradeoff in latency and reliability

– As p ⬆, latency ⬇

– As p ⬆, fraction of nodes receiving a broadcast ⬇(unless q = 1)

• q represents a tradeoff in energy and reliability

– As q ⬆, energy consumption ⬆

– As q ⬆, fraction of nodes receiving a broadcast ⬆ (unless p = 0)

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Experimental Data

• Assumptions:

– Ideal MAC layer

– Ideal physical layer with no collisions or interference

• IEEE 802.11 PSM

• Grid network topology (i.e., a square lattice)

• Broadcast source is near the center of the grid

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• N is the number of nodes• λ is the source’s broadcast

rate• PTX is power to transmit• PI is power to idle/receive• PS is power to sleep• L1 is the latency

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Open Road

Closed Road

D Destination

D

S Source

S

Bond Percolation Theory

Reliability

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Reliability

≥80% Reliability≥90% Reliability≥99% Reliability≈100% Reliability

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Average Energy Consumption

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Latency

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S

D20

Hop

s

Latency

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Energy-Latency Tradeoff

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Energy-Latency Trade-off

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Code Distribution Application

• Study Trade Off Between Energy, Latency, and Reliability

• ns-2 network simulator• Collisions and interference present

Discussion

• Why use IEEE 802.11 PSM for simulation results?

• How well would this work for other protocols like S-MAC and T-MAC?

• When studying reliability, why use Bond percolation theory over Site percolation theory?

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